R Notebook

Variables:

Risk
Money
Security
Good time Help Success Proper Environment Tradition Creativity

Friends important Family important Leisure time Happiness Health (subjective) Satisfaction Freedom

Sex Age Country Wave Marital status Children Employment Education

library(data.table)
library(tidyr)

#read the data (Wave 5)

# Data of Wave 5


WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")


# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
head(WV5_data_df[, 1:5])

clean the data set

library(dplyr)

#rename the variables
WV5_data <- WV5_data_df %>%
  rename(sex = V235, age = V237, country = V2, wave = V1, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V22, freedom = V46, marital_status = V55, children = V56, creativity = V80, money = V81, security = V82, goodtime = V83, help = V84, success = V85, risk = V86, proper = V87, environment = V88, tradition = V89, employment = V241, education = V238)
WV5_data

#select only the variables of interest
WV5_data <- WV5_data %>%
  select(sex, age, country, wave, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom, marital_status, children, creativity, money, security, goodtime, help, success, risk, proper, environment, tradition, employment, education)
WV5_data

#decode the country names 
countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV5_data$country_lab = countrynames$name [match(WV5_data$country, countrynames$code)]
table(WV5_data$country_lab)

            Andorra           Argentina           Australia 
               1003                1002                1421 
             Brazil            Bulgaria        Burkina Faso 
               1500                1001                1534 
             Canada               Chile               China 
               2164                1000                1991 
           Colombia          Cyprus (G)               Egypt 
               3025                1050                3051 
           Ethiopia             Finland              France 
               1500                1014                1001 
            Georgia             Germany               Ghana 
               1500                2064                1534 
      Great Britain           Guatemala           Hong Kong 
               1041                1000                1252 
            Hungary               India           Indonesia 
               1007                2001                2015 
               Iran                Iraq               Italy 
               2667                2701                1012 
              Japan              Jordan            Malaysia 
               1096                1200                1201 
               Mali              Mexico             Moldova 
               1534                1560                1046 
            Morocco         Netherlands         New Zealand 
               1200                1050                 954 
             Norway                Peru              Poland 
               1025                1500                1000 
            Romania              Russia              Rwanda 
               1776                2033                1507 
           Slovenia        South Africa         South Korea 
               1037                2988                1200 
              Spain              Sweden         Switzerland 
               1200                1003                1241 
             Taiwan            Thailand Trinidad and Tobago 
               1227                1534                1002 
             Turkey             Ukraine       United States 
               1346                1000                1249 
            Uruguay            Viet Nam              Zambia 
               1000                1495                1500 

WV5_data

NA
NA

#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)

` ``{r} #rename variables

WV6_data <- WV6_data %>%
  rename(wave = V1, sex = V240, age = V242,country = V2, marital_status = V57, children = V58, employment = V229, education = V248, risk = V76, money = V71, security = V72, goodtime =  V73, help = V74B, success = V75, proper = V77, environment = V78, tradition = V79, creativity = V70, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V23, freedom = V55 )


#select only the variables of interest
WV6_data <- WV6_data %>%
  select(sex, age, country, wave, marital_status, children, employment, education, risk, money, security, goodtime, help, success, proper, environment, tradition, creativity, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom)
WV6_data

NA

#decode daraset (Wave 6)

countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country_lab = countrynames$name [match(WV6_data$country, countrynames$code)]
table(WV6_data$country_lab)

            Algeria           Argentina             Armenia 
               1200                1030                1100 
          Australia          Azerbaijan             Belarus 
               1477                1002                1535 
             Brazil               Chile               China 
               1486                1000                2300 
           Colombia          Cyprus (G)             Ecuador 
               1512                1000                1202 
              Egypt             Estonia             Georgia 
               1523                1533                1202 
            Germany               Ghana               Haiti 
               2046                1552                1996 
          Hong Kong               India                Iraq 
               1000                4078                1200 
              Japan              Jordan          Kazakhstan 
               2443                1200                1500 
             Kuwait          Kyrgyzstan             Lebanon 
               1303                1500                1200 
              Libya            Malaysia              Mexico 
               2131                1300                2000 
            Morocco         Netherlands         New Zealand 
               1200                1902                 841 
            Nigeria            Pakistan           Palestine 
               1759                1200                1000 
               Peru         Philippines              Poland 
               1210                1200                 966 
              Qatar             Romania              Russia 
               1060                1503                2500 
             Rwanda           Singapore            Slovenia 
               1527                1972                1069 
       South Africa         South Korea               Spain 
               3531                1200                1189 
             Sweden              Taiwan            Thailand 
               1206                1238                1200 
Trinidad and Tobago             Tunisia              Turkey 
                999                1205                1605 
            Ukraine       United States             Uruguay 
               1500                2232                1000 
         Uzbekistan               Yemen            Zimbabwe 
               1500                1000                1500 

WV6_data

#combine the 2 dataset (Wave 6 + Wave 5)

WV5_data

WV6_data

data = rbind(WV5_data, WV6_data)
data

#number of countries

length(unique(data$country_lab))

[1] 80

#number of participants

nrow(data)

[1] 173540

#exclusion of participants

data = subset(data, risk > 0 & sex > 0 & age > 0 & education > 0 & employment > 0 & marital_status > 0 & children >= 0 & family_important > 0 & friends_important > 0 & leisure_time > 0 & happiness > 0 & health > 0 & satisfaction > 0 & freedom > 0 & marital_status > 0 & creativity > 0 & money > 0 & security > 0 & goodtime >0 & help > 0 & success > 0, risk > 0 & proper > 0 & environment > 0 & tradition > 0 & employment > 0 & education > 0) 

 

data

#number of males vs females (1 = males; 2 = females)

table(data$sex)

    1     2 
71689 77937 

#create a categorical age variable

data$agecat[data$age<20]="15-19"
data$agecat[data$age>=20 & data$age <30] = "20-29"
data$agecat[data$age>=30 & data$age <40] = "30-39"
data$agecat[data$age>=40 & data$age <50] = "40-49"
data$agecat[data$age>=50 & data$age <60] = "50-59"
data$agecat[data$age>=60 & data$age <70] = "60-69"
data$agecat[data$age>=70 & data$age <80] = "70-79"
data$agecat[data$age>=80] = "80+"

#gender variables

data$sex[data$sex == 1] <- "male"
data$sex[data$sex == 2] <- "female"

#average age of participants

mean(data$age)

[1] 41.59569

#age range

range(data$age) 

[1] 15 99

#risk taking Frequency

library(ggplot2)
ggplot(data, aes(x = risk)) +
  geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
  labs(x = "Risk Taking", y = "Frequency", title = "Histogram of Risk Taking") +
  theme_minimal()

#age frequency

ggplot(data, aes(x = age)) +
  geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
  labs(x = "Age", y = "Frequency", title = "Histogram of Age Distributionn") +
  theme_minimal()

#age vs risk taking

ggplot(data, aes(x = agecat, y = risk)) +
  geom_boxplot() +
  labs(title = "Boxplot of Risk and Adventure by Age",
       x = "Age",
       y = "Risk and Adventure") +
  theme_minimal()

NA
NA

#sex vs risk taking

ggplot(data, aes(as.factor(sex), risk))+
  geom_boxplot()

summary(data)

     sex                 age          country           wave       family_important friends_important  leisure_time      happiness          health        satisfaction   
 Length:149626      Min.   :15.0   Min.   : 12.0   Min.   :5.000   Min.   :-5.000   Min.   :-5.000    Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000  
 Class :character   1st Qu.:28.0   1st Qu.:276.0   1st Qu.:5.000   1st Qu.: 1.000   1st Qu.: 1.000    1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 5.000  
 Mode  :character   Median :39.0   Median :484.0   Median :6.000   Median : 1.000   Median : 2.000    Median : 2.000   Median : 2.000   Median : 2.000   Median : 7.000  
                    Mean   :41.6   Mean   :481.5   Mean   :5.552   Mean   : 1.094   Mean   : 1.661    Mean   : 1.871   Mean   : 1.865   Mean   : 2.106   Mean   : 6.755  
                    3rd Qu.:53.0   3rd Qu.:710.0   3rd Qu.:6.000   3rd Qu.: 1.000   3rd Qu.: 2.000    3rd Qu.: 2.000   3rd Qu.: 2.000   3rd Qu.: 3.000   3rd Qu.: 8.000  
                    Max.   :99.0   Max.   :894.0   Max.   :6.000   Max.   : 4.000   Max.   : 4.000    Max.   : 4.000   Max.   : 4.000   Max.   : 5.000   Max.   :10.000  
                                                                   NA's   :221      NA's   :351       NA's   :698      NA's   :573      NA's   :230      NA's   :340     
    freedom       marital_status     children       creativity         money           security         goodtime           help          success            risk      
 Min.   :-5.000   Min.   :1.000   Min.   :0.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.00   Min.   :-5.000   Min.   :1.000  
 1st Qu.: 6.000   1st Qu.:1.000   1st Qu.:0.000   1st Qu.: 2.000   1st Qu.: 3.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.: 1.00   1st Qu.: 2.000   1st Qu.:3.000  
 Median : 7.000   Median :1.000   Median :2.000   Median : 3.000   Median : 4.000   Median : 2.000   Median : 3.000   Median : 2.00   Median : 3.000   Median :4.000  
 Mean   : 7.004   Mean   :2.715   Mean   :1.843   Mean   : 2.718   Mean   : 3.846   Mean   : 2.374   Mean   : 3.273   Mean   : 2.29   Mean   : 2.951   Mean   :3.801  
 3rd Qu.: 9.000   3rd Qu.:6.000   3rd Qu.:3.000   3rd Qu.: 4.000   3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 5.000   3rd Qu.: 3.00   3rd Qu.: 4.000   3rd Qu.:5.000  
 Max.   :10.000   Max.   :6.000   Max.   :8.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.00   Max.   : 6.000   Max.   :6.000  
 NA's   :838                                      NA's   :972      NA's   :602      NA's   :442      NA's   :566      NA's   :44862   NA's   :703                     
     proper        environment       tradition        employment      education     country_lab           agecat         
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :1.000   Min.   :1.000   Length:149626      Length:149626     
 1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.:1.000   1st Qu.:3.000   Class :character   Class :character  
 Median : 2.000   Median : 2.000   Median : 2.000   Median :3.000   Median :5.000   Mode  :character   Mode  :character  
 Mean   : 2.533   Mean   : 2.468   Mean   : 2.511   Mean   :3.406   Mean   :5.501                                        
 3rd Qu.: 3.000   3rd Qu.: 3.000   3rd Qu.: 3.000   3rd Qu.:5.000   3rd Qu.:7.000                                        
 Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   :8.000   Max.   :9.000                                        
 NA's   :541      NA's   :561      NA's   :518                                                                           

#data cleaning: deletion of NAs 
data = na.omit(data)
summary(data)

     sex                 age           country           wave       family_important friends_important  leisure_time   
 Length:101172      Min.   :15.00   Min.   : 12.0   Min.   :5.000   Min.   :-5.000   Min.   :-5.000    Min.   :-5.000  
 Class :character   1st Qu.:27.00   1st Qu.:268.0   1st Qu.:5.000   1st Qu.: 1.000   1st Qu.: 1.000    1st Qu.: 1.000  
 Mode  :character   Median :39.00   Median :458.0   Median :5.000   Median : 1.000   Median : 2.000    Median : 2.000  
                    Mean   :41.11   Mean   :474.4   Mean   :5.348   Mean   : 1.099   Mean   : 1.652    Mean   : 1.893  
                    3rd Qu.:53.00   3rd Qu.:710.0   3rd Qu.:6.000   3rd Qu.: 1.000   3rd Qu.: 2.000    3rd Qu.: 2.000  
                    Max.   :99.00   Max.   :894.0   Max.   :6.000   Max.   : 4.000   Max.   : 4.000    Max.   : 4.000  
   happiness          health        satisfaction       freedom      marital_status     children       creativity    
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.00   Min.   :1.000   Min.   :0.000   Min.   :-5.000  
 1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 5.000   1st Qu.: 5.00   1st Qu.:1.000   1st Qu.:0.000   1st Qu.: 2.000  
 Median : 2.000   Median : 2.000   Median : 7.000   Median : 7.00   Median :1.000   Median :2.000   Median : 2.000  
 Mean   : 1.889   Mean   : 2.098   Mean   : 6.692   Mean   : 6.91   Mean   :2.769   Mean   :1.835   Mean   : 2.699  
 3rd Qu.: 2.000   3rd Qu.: 3.000   3rd Qu.: 8.000   3rd Qu.: 9.00   3rd Qu.:6.000   3rd Qu.:3.000   3rd Qu.: 4.000  
 Max.   : 4.000   Max.   : 5.000   Max.   :10.000   Max.   :10.00   Max.   :6.000   Max.   :8.000   Max.   : 6.000  
     money           security         goodtime           help           success            risk           proper      
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :1.000   Min.   :-5.000  
 1st Qu.: 3.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.:3.000   1st Qu.: 1.000  
 Median : 4.000   Median : 2.000   Median : 3.000   Median : 2.000   Median : 3.000   Median :4.000   Median : 2.000  
 Mean   : 3.842   Mean   : 2.363   Mean   : 3.243   Mean   : 2.281   Mean   : 2.937   Mean   :3.827   Mean   : 2.538  
 3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 4.000   3rd Qu.:5.000   3rd Qu.: 3.000  
 Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   :6.000   Max.   : 6.000  
  environment       tradition       employment      education     country_lab           agecat          education_cat     
 Min.   :-5.000   Min.   :-5.00   Min.   :1.000   Min.   :1.000   Length:101172      Length:101172      Length:101172     
 1st Qu.: 2.000   1st Qu.: 1.00   1st Qu.:1.000   1st Qu.:3.000   Class :character   Class :character   Class :character  
 Median : 2.000   Median : 2.00   Median :3.000   Median :5.000   Mode  :character   Mode  :character   Mode  :character  
 Mean   : 2.452   Mean   : 2.51   Mean   :3.467   Mean   :5.309                                                           
 3rd Qu.: 3.000   3rd Qu.: 3.00   3rd Qu.:5.000   3rd Qu.:7.000                                                           
 Max.   : 6.000   Max.   : 6.00   Max.   :8.000   Max.   :9.000                                                           

#ris vs education
ggplot(data, aes(risk, education))+
  geom_point()+
  geom_smooth(method = "lm")

model = lm(risk ~ education, data = data)
summary(model)

Call:
lm(formula = risk ~ education, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0532 -1.0532  0.1564  1.2612  2.3660 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.10560    0.01183  347.08   <2e-16 ***
education   -0.05240    0.00202  -25.95   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.589 on 101170 degrees of freedom
Multiple R-squared:  0.00661,   Adjusted R-squared:  0.0066 
F-statistic: 673.1 on 1 and 101170 DF,  p-value: < 2.2e-16

ggplot(data, aes(risk, freedom))+
  geom_point()+
  geom_smooth(method = "lm")

model1 = lm(risk ~ freedom, data = data)
summary(model1)

Call:
lm(formula = risk ~ freedom, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3968 -1.1100  0.1769  1.2247  2.3204 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.157773   0.014987  277.43   <2e-16 ***
freedom     -0.047814   0.002045  -23.38   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.59 on 101170 degrees of freedom
Multiple R-squared:  0.005375,  Adjusted R-squared:  0.005365 
F-statistic: 546.7 on 1 and 101170 DF,  p-value: < 2.2e-16

ggplot(data, aes(as.factor(wave), risk))+
  geom_boxplot()

ggplot(data, aes(risk, age))+
  geom_point()+
  geom_smooth(method = "lm")

attach(data)
data$education_cat[education < 3] = "incomplete or no primary education"
data$education_cat[education > 2 & education <= 6] <- "no uni"
data$education_cat[education >= 7] <- "uni"
detach(data)
table(data$education)

    1     2     3     4     5     6     7     8     9 
 9751  9603 18657 11323 29208 10845 24715 10923 24601 

data

data$wave[data$wave == 5] <- "Wave 5"
data$sex[data$wave == 6] <- "Wave 6"
data

# Check the unique responses of each variable with frequencies
for (col_name in names(data)) {
  response_table <- table(data[[col_name]])
  print(paste("Response frequencies for", col_name, ":"))
  print(response_table)
}

[1] "Response frequencies for sex :"

female   male 
 52327  48845 
[1] "Response frequencies for age :"

  15   16   17   18   19   20   21   22   23   24   25 
  56  374  528 2379 2234 2592 2274 2519 2573 2492 2615 
  26   27   28   29   30   31   32   33   34   35   36 
2365 2481 2415 2210 2738 1998 2264 2047 2049 2540 2090 
  37   38   39   40   41   42   43   44   45   46   47 
1993 2146 1931 2535 1720 2184 1899 1793 2132 1667 1637 
  48   49   50   51   52   53   54   55   56   57   58 
1668 1592 2008 1415 1592 1400 1405 1595 1363 1342 1217 
  59   60   61   62   63   64   65   66   67   68   69 
1037 1453  989 1138 1046  899 1164  885  791  810  652 
  70   71   72   73   74   75   76   77   78   79   80 
 924  554  624  539  478  488  434  372  312  230  263 
  81   82   83   84   85   86   87   88   89   90   91 
 193  180  149  129  125   65   45   29   29   29   12 
  92   93   94   95   97   98   99 
   8    6   10    5    4    3    2 
[1] "Response frequencies for country :"

  12   20   32   36   76  100  124  152  156  158  170 
 934 1001  978 1336 2867  935 2094  963 3664 1225 1458 
 196  218  231  246  250  268  275  276  288  332  348 
1029 1196 1479 1004  993 2600  918 2931 1513 1868  999 
 356  360  364  368  392  400  410  414  422  434  458 
1320 1922 2545 1093 2598 1178 1199  985 1000 1781 1197 
 466  484  498  504  528  578  586  604  616  642  643 
1187 1500 1028 1803 2566 1015 1108 1421  986 1530 1907 
 646  702  704  705  710  724  752  756  764  780  788 
2573 1923 1406 1001 6241 2216  985 1219 2548  995 1024 
 792  804  818  826  854  858  887  894 
1302  939 4549 1006 1250  983  739 1419 
[1] "Response frequencies for wave :"

    5     6 
65923 35249 
[1] "Response frequencies for family_important :"

   -5    -2    -1     1     2     3     4 
   33    89    65 91729  8132   852   272 
[1] "Response frequencies for friends_important :"

   -5    -2    -1     1     2     3     4 
   26   178   151 47663 41137 10256  1761 
[1] "Response frequencies for leisure_time :"

   -5    -2    -1     1     2     3     4 
   56   347   314 34842 43129 18251  4233 
[1] "Response frequencies for happiness :"

   -5    -2    -1     1     2     3     4 
   17   177   434 29505 53848 13997  3194 
[1] "Response frequencies for health :"

   -5    -2    -1     1     2     3     4     5 
    5   148    81 25633 45401 23491  6359    54 
[1] "Response frequencies for satisfaction :"

   -5    -2    -1     1     2     3     4     5     6 
   25   340   252  3324  2249  4386  5420 13413 11842 
    7     8     9    10 
16887 20550 10143 12341 
[1] "Response frequencies for freedom :"

   -5    -2    -1     1     2     3     4     5     6 
   45   335   873  2981  1639  3126  4564 12576 11942 
    7     8     9    10 
16907 18755 10284 17145 
[1] "Response frequencies for marital_status :"

    1     2     3     4     5     6 
55216  7851  3296  1875  5813 27121 
[1] "Response frequencies for children :"

    0     1     2     3     4     5     6     7     8 
31045 16203 24841 13858  6859  3656  2047  1146  1517 
[1] "Response frequencies for creativity :"

   -5    -2    -1     1     2     3     4     5     6 
   15   193   655 20992 29096 23097 13553  9933  3638 
[1] "Response frequencies for money :"

   -5    -2    -1     1     2     3     4     5     6 
   16   148   302  8513 14253 17681 17200 27821 15238 
[1] "Response frequencies for security :"

   -5    -2    -1     1     2     3     4     5     6 
   10   183   276 30142 31845 19335 11079  6237  2065 
[1] "Response frequencies for goodtime :"

   -5    -2    -1     1     2     3     4     5     6 
   19   252   275 13965 22171 21433 17525 16879  8653 
[1] "Response frequencies for help :"

   -5    -2    -1     1     2     3     4     5     6 
   11   185   183 27950 35868 21809 10599  3503  1064 
[1] "Response frequencies for success :"

   -5    -2    -1     1     2     3     4     5     6 
   16   239   389 17947 25555 22555 16029 13450  4992 
[1] "Response frequencies for risk :"

    1     2     3     4     5     6 
 9808 15160 17160 16901 24844 17299 
[1] "Response frequencies for proper :"

   -5    -2    -1     1     2     3     4     5     6 
   11   175   296 25321 31522 20186 12496  8388  2777 
[1] "Response frequencies for environment :"

   -5    -2    -1     1     2     3     4     5     6 
   24   157   459 24324 33665 22423 12704  5218  2198 
[1] "Response frequencies for tradition :"

   -5    -2    -1     1     2     3     4     5     6 
   18   155   248 29740 28611 18479 11569  8246  4106 
[1] "Response frequencies for employment :"

    1     2     3     4     5     6     7     8 
31967  8190 13340 11846 15128  7656 10934  2111 
[1] "Response frequencies for education :"

    1     2     3     4     5     6     7     8     9 
 7780  7420 14060  7904 18308  7321 16517  7162 14700 
[1] "Response frequencies for country_lab :"

            Algeria             Andorra 
                934                1001 
          Argentina           Australia 
                978                1336 
             Brazil            Bulgaria 
               2867                 935 
       Burkina Faso              Canada 
               1250                2094 
              Chile               China 
                963                3664 
           Colombia          Cyprus (G) 
               1458                1029 
            Ecuador               Egypt 
               1196                4549 
           Ethiopia             Finland 
               1479                1004 
             France             Georgia 
                993                2600 
            Germany               Ghana 
               2931                1513 
      Great Britain               Haiti 
               1006                1868 
            Hungary               India 
                999                1320 
          Indonesia                Iran 
               1922                2545 
               Iraq               Japan 
               1093                2598 
             Jordan              Kuwait 
               1178                 985 
            Lebanon               Libya 
               1000                1781 
           Malaysia                Mali 
               1197                1187 
             Mexico             Moldova 
               1500                1028 
            Morocco         Netherlands 
               1803                2566 
             Norway            Pakistan 
               1015                1108 
          Palestine                Peru 
                918                1421 
             Poland             Romania 
                986                1530 
             Russia              Rwanda 
               1907                2573 
          Singapore            Slovenia 
               1923                1001 
       South Africa         South Korea 
               6241                1199 
              Spain              Sweden 
               2216                 985 
        Switzerland              Taiwan 
               1219                1225 
           Thailand Trinidad and Tobago 
               2548                 995 
            Tunisia              Turkey 
               1024                1302 
            Ukraine             Uruguay 
                939                 983 
           Viet Nam               Yemen 
               1406                 739 
             Zambia 
               1419 
[1] "Response frequencies for agecat :"

15-19 20-29 30-39 40-49 50-59 60-69 70-79   80+ 
 5571 24536 21796 18827 14374  9827  4955  1286 
[1] "Response frequencies for education_cat :"

incomplete or no primary education 
                             15200 
                            no uni 
                             47593 
                               uni 
                             38379 

```